Diabetic retinopathy (DR) is the leading cause of blindness in the working population of the western world. It is an eye disease which in some form afflicts 17% of diabetic subjects five years after diagnosis of diabetes and 97% fifteen years after diagnosis. Early diagnosis through regular screening and timely treatment has been shown to prevent visual loss and blindness. Digital color fundus photography allows acquisition of fundus images (see 500 in FIG. 5) in a non-invasive manner which is a prerequisite for large scale screening.
In a DR screening program, the number of fundus images that need to be examined by ophthalmologists can be prohibitively large. The number of images without any sign of DR in a screening setting is typically over 90%. Therefore an automated system that can decide whether or not any signs suspicious for DR are present in an image can improve efficiency; only those images deemed suspect by the system would require examination by an ophthalmologist.
Signs of DR include red lesions such as microaneurysms and intraretinal hemorrhages, and white lesions, such as exudates and cottonwool spots. Red lesions are among the first unequivocal signs of DR. Therefore, their detection is critical for a pre-screening system.
Previously published methods for the detection of red lesions have focused on detecting microaneurysms in fluorescein angiography images of the fundus. In this type of image, the contrast between the microaneurysms and background is larger than in digital color images. However, the mortality of 1:222,000 associated with the intravenous use of fluorescein prohibits the application of this technique for large-scale screening purposes.
The detection method described by Spencer, Cree, Frame and co-workers employed a mathematical morphology technique that eliminates the vasculature from a fundus image yet left possible microaneurysm candidates untouched. Spencer, J. Olson, K. McHardy, P. Sharp, and J. Forrester, “An image-processing strategy for the segmentation and quantification in fluorescein angiograms of the ocular fundus,” Computers and biomedical research, vol. 29, pp. 284-302, 1996. A. Frame, P. Undrill, M. Cree, J. Olson, K. McHardy, P. Sharp, and J. Forrester, “A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms,” Computers in Biology and Medicine, vol. 28, pp. 225-238, 1998, all incorporated by reference in their entireties. It has also been applied, in a modified version, to high resolution red-free fundus images with a sensitivity of 85% and a specificity of 76% on a per image basis.
A number of other approaches for the detection of red lesions in color fundus images have also been described. A neural network has been used to detect both hemorrhages and exudates. Each image was divided into 20×20 pixel grids; these were then individually classified. The per image results showed a sensitivity of 88.4% and a specificity of 83.5%.
A recursive region-growing procedure has been applied to segment both the vessels and red lesions in a fundus image. Next, a neural network was used to detect the vessels exclusively. The objects that remain after removal of the detected vasculature are labeled as microaneurysms. The evaluation was carried out on 10×10 pixel grids and not for individual images or lesions. A sensitivity of 77.5% and specificity of 88.7% were reported.
Results of a commercially available automatic red lesion detection system have been released. However, their method was not described. The system had a sensitivity of 93.1% and specificity of 71.4% on a per patient basis. Because of the inadequacy of the aforementioned techniques, there exists a great need for a screening system that can identify lesions suspicious for Diabetic Retinopathy.